Overview

Dataset statistics

Number of variables21
Number of observations418
Missing cells745
Missing cells (%)8.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory263.1 KiB
Average record size in memory644.6 B

Variable types

Numeric7
Unsupported1
Categorical10
Text3

Alerts

DatasetName has constant value ""Constant
Survived has 418 (100.0%) missing valuesMissing
Cabin has 327 (78.2%) missing valuesMissing
PassengerId is uniformly distributedUniform
PassengerId has unique valuesUnique
Name has unique valuesUnique
Survived is an unsupported type, check if it needs cleaning or further analysisUnsupported
SibSp has 283 (67.7%) zerosZeros
Parch has 324 (77.5%) zerosZeros

Reproduction

Analysis started2024-03-21 12:54:02.942514
Analysis finished2024-03-21 12:54:10.620976
Duration7.68 seconds
Software versionydata-profiling vv4.6.5
Download configurationconfig.json

Variables

PassengerId
Real number (ℝ)

UNIFORM  UNIQUE 

Distinct418
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1100.5
Minimum892
Maximum1309
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.4 KiB
2024-03-21T09:54:10.748604image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum892
5-th percentile912.85
Q1996.25
median1100.5
Q31204.75
95-th percentile1288.15
Maximum1309
Range417
Interquartile range (IQR)208.5

Descriptive statistics

Standard deviation120.81046
Coefficient of variation (CV)0.10977779
Kurtosis-1.2
Mean1100.5
Median Absolute Deviation (MAD)104.5
Skewness0
Sum460009
Variance14595.167
MonotonicityStrictly increasing
2024-03-21T09:54:10.946102image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
892 1
 
0.2%
1205 1
 
0.2%
1177 1
 
0.2%
1176 1
 
0.2%
1175 1
 
0.2%
1174 1
 
0.2%
1173 1
 
0.2%
1172 1
 
0.2%
1171 1
 
0.2%
1170 1
 
0.2%
Other values (408) 408
97.6%
ValueCountFrequency (%)
892 1
0.2%
893 1
0.2%
894 1
0.2%
895 1
0.2%
896 1
0.2%
897 1
0.2%
898 1
0.2%
899 1
0.2%
900 1
0.2%
901 1
0.2%
ValueCountFrequency (%)
1309 1
0.2%
1308 1
0.2%
1307 1
0.2%
1306 1
0.2%
1305 1
0.2%
1304 1
0.2%
1303 1
0.2%
1302 1
0.2%
1301 1
0.2%
1300 1
0.2%

Survived
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing418
Missing (%)100.0%
Memory size3.4 KiB

Pclass
Categorical

Distinct3
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size23.8 KiB
3
218 
1
107 
2
93 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters418
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row3
3rd row2
4th row3
5th row3

Common Values

ValueCountFrequency (%)
3 218
52.2%
1 107
25.6%
2 93
22.2%

Length

2024-03-21T09:54:11.115653image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-21T09:54:11.266253image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
3 218
52.2%
1 107
25.6%
2 93
22.2%

Most occurring characters

ValueCountFrequency (%)
3 218
52.2%
1 107
25.6%
2 93
22.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 418
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 218
52.2%
1 107
25.6%
2 93
22.2%

Most occurring scripts

ValueCountFrequency (%)
Common 418
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3 218
52.2%
1 107
25.6%
2 93
22.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 418
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 218
52.2%
1 107
25.6%
2 93
22.2%

Name
Text

UNIQUE 

Distinct418
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size34.6 KiB
2024-03-21T09:54:11.503584image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length63
Median length51
Mean length27.483254
Min length13

Characters and Unicode

Total characters11488
Distinct characters58
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique418 ?
Unique (%)100.0%

Sample

1st rowKelly, Mr. James
2nd rowWilkes, Mrs. James (Ellen Needs)
3rd rowMyles, Mr. Thomas Francis
4th rowWirz, Mr. Albert
5th rowHirvonen, Mrs. Alexander (Helga E Lindqvist)
ValueCountFrequency (%)
mr 242
 
14.0%
miss 78
 
4.5%
mrs 72
 
4.2%
john 28
 
1.6%
william 23
 
1.3%
master 21
 
1.2%
charles 16
 
0.9%
joseph 15
 
0.9%
james 14
 
0.8%
henry 14
 
0.8%
Other values (825) 1202
69.7%
2024-03-21T09:54:11.970335image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1309
 
11.4%
r 971
 
8.5%
e 822
 
7.2%
a 786
 
6.8%
s 628
 
5.5%
i 621
 
5.4%
n 596
 
5.2%
l 526
 
4.6%
M 515
 
4.5%
o 467
 
4.1%
Other values (48) 4247
37.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 7395
64.4%
Uppercase Letter 1738
 
15.1%
Space Separator 1309
 
11.4%
Other Punctuation 884
 
7.7%
Open Punctuation 78
 
0.7%
Close Punctuation 78
 
0.7%
Dash Punctuation 6
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r 971
13.1%
e 822
11.1%
a 786
10.6%
s 628
8.5%
i 621
8.4%
n 596
8.1%
l 526
 
7.1%
o 467
 
6.3%
t 303
 
4.1%
h 257
 
3.5%
Other values (16) 1418
19.2%
Uppercase Letter
ValueCountFrequency (%)
M 515
29.6%
J 112
 
6.4%
A 103
 
5.9%
C 101
 
5.8%
E 95
 
5.5%
S 81
 
4.7%
H 80
 
4.6%
W 76
 
4.4%
B 69
 
4.0%
L 61
 
3.5%
Other values (14) 445
25.6%
Other Punctuation
ValueCountFrequency (%)
. 418
47.3%
, 418
47.3%
" 44
 
5.0%
' 4
 
0.5%
Space Separator
ValueCountFrequency (%)
1309
100.0%
Open Punctuation
ValueCountFrequency (%)
( 78
100.0%
Close Punctuation
ValueCountFrequency (%)
) 78
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 9133
79.5%
Common 2355
 
20.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
r 971
 
10.6%
e 822
 
9.0%
a 786
 
8.6%
s 628
 
6.9%
i 621
 
6.8%
n 596
 
6.5%
l 526
 
5.8%
M 515
 
5.6%
o 467
 
5.1%
t 303
 
3.3%
Other values (40) 2898
31.7%
Common
ValueCountFrequency (%)
1309
55.6%
. 418
 
17.7%
, 418
 
17.7%
( 78
 
3.3%
) 78
 
3.3%
" 44
 
1.9%
- 6
 
0.3%
' 4
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 11488
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1309
 
11.4%
r 971
 
8.5%
e 822
 
7.2%
a 786
 
6.8%
s 628
 
5.5%
i 621
 
5.4%
n 596
 
5.2%
l 526
 
4.6%
M 515
 
4.5%
o 467
 
4.1%
Other values (48) 4247
37.0%

Sex
Categorical

Distinct2
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size25.3 KiB
male
266 
female
152 

Length

Max length6
Median length4
Mean length4.7272727
Min length4

Characters and Unicode

Total characters1976
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowmale
2nd rowfemale
3rd rowmale
4th rowmale
5th rowfemale

Common Values

ValueCountFrequency (%)
male 266
63.6%
female 152
36.4%

Length

2024-03-21T09:54:12.169802image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-21T09:54:12.332402image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
male 266
63.6%
female 152
36.4%

Most occurring characters

ValueCountFrequency (%)
e 570
28.8%
m 418
21.2%
a 418
21.2%
l 418
21.2%
f 152
 
7.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1976
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 570
28.8%
m 418
21.2%
a 418
21.2%
l 418
21.2%
f 152
 
7.7%

Most occurring scripts

ValueCountFrequency (%)
Latin 1976
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 570
28.8%
m 418
21.2%
a 418
21.2%
l 418
21.2%
f 152
 
7.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1976
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 570
28.8%
m 418
21.2%
a 418
21.2%
l 418
21.2%
f 152
 
7.7%

Age
Real number (ℝ)

Distinct81
Distinct (%)19.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29.398325
Minimum0.17
Maximum76
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.4 KiB
2024-03-21T09:54:12.492937image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0.17
5-th percentile8
Q121.25
median26
Q336.375
95-th percentile55
Maximum76
Range75.83
Interquartile range (IQR)15.125

Descriptive statistics

Standard deviation13.233076
Coefficient of variation (CV)0.45013027
Kurtosis0.50350363
Mean29.398325
Median Absolute Deviation (MAD)6.25
Skewness0.57058734
Sum12288.5
Variance175.1143
MonotonicityNot monotonic
2024-03-21T09:54:12.691408image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
26 58
 
13.9%
18 28
 
6.7%
30 19
 
4.5%
24 17
 
4.1%
21 17
 
4.1%
22 16
 
3.8%
31 13
 
3.1%
27 12
 
2.9%
23 11
 
2.6%
45 11
 
2.6%
Other values (71) 216
51.7%
ValueCountFrequency (%)
0.17 1
 
0.2%
0.33 1
 
0.2%
0.75 1
 
0.2%
0.83 1
 
0.2%
0.92 1
 
0.2%
1 3
0.7%
2 2
 
0.5%
3 1
 
0.2%
5 1
 
0.2%
6 7
1.7%
ValueCountFrequency (%)
76 1
 
0.2%
67 1
 
0.2%
64 3
0.7%
63 2
0.5%
62 1
 
0.2%
61 2
0.5%
60.5 1
 
0.2%
60 3
0.7%
59 1
 
0.2%
58 1
 
0.2%

SibSp
Real number (ℝ)

ZEROS 

Distinct7
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.44736842
Minimum0
Maximum8
Zeros283
Zeros (%)67.7%
Negative0
Negative (%)0.0%
Memory size3.4 KiB
2024-03-21T09:54:12.856965image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile2
Maximum8
Range8
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.89675956
Coefficient of variation (CV)2.0045214
Kurtosis26.498712
Mean0.44736842
Median Absolute Deviation (MAD)0
Skewness4.1683366
Sum187
Variance0.80417771
MonotonicityNot monotonic
2024-03-21T09:54:13.016536image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 283
67.7%
1 110
 
26.3%
2 14
 
3.3%
3 4
 
1.0%
4 4
 
1.0%
8 2
 
0.5%
5 1
 
0.2%
ValueCountFrequency (%)
0 283
67.7%
1 110
 
26.3%
2 14
 
3.3%
3 4
 
1.0%
4 4
 
1.0%
5 1
 
0.2%
8 2
 
0.5%
ValueCountFrequency (%)
8 2
 
0.5%
5 1
 
0.2%
4 4
 
1.0%
3 4
 
1.0%
2 14
 
3.3%
1 110
 
26.3%
0 283
67.7%

Parch
Real number (ℝ)

ZEROS 

Distinct8
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.3923445
Minimum0
Maximum9
Zeros324
Zeros (%)77.5%
Negative0
Negative (%)0.0%
Memory size3.4 KiB
2024-03-21T09:54:13.161151image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2
Maximum9
Range9
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.98142888
Coefficient of variation (CV)2.5014468
Kurtosis31.412513
Mean0.3923445
Median Absolute Deviation (MAD)0
Skewness4.6544617
Sum164
Variance0.96320264
MonotonicityNot monotonic
2024-03-21T09:54:13.307758image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0 324
77.5%
1 52
 
12.4%
2 33
 
7.9%
3 3
 
0.7%
4 2
 
0.5%
9 2
 
0.5%
6 1
 
0.2%
5 1
 
0.2%
ValueCountFrequency (%)
0 324
77.5%
1 52
 
12.4%
2 33
 
7.9%
3 3
 
0.7%
4 2
 
0.5%
5 1
 
0.2%
6 1
 
0.2%
9 2
 
0.5%
ValueCountFrequency (%)
9 2
 
0.5%
6 1
 
0.2%
5 1
 
0.2%
4 2
 
0.5%
3 3
 
0.7%
2 33
 
7.9%
1 52
 
12.4%
0 324
77.5%

Ticket
Text

Distinct363
Distinct (%)86.8%
Missing0
Missing (%)0.0%
Memory size26.2 KiB
2024-03-21T09:54:13.566068image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length18
Median length17
Mean length6.8755981
Min length3

Characters and Unicode

Total characters2874
Distinct characters32
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique321 ?
Unique (%)76.8%

Sample

1st row330911
2nd row363272
3rd row240276
4th row315154
5th row3101298
ValueCountFrequency (%)
pc 32
 
5.9%
c.a 19
 
3.5%
ca 8
 
1.5%
soton/o.q 8
 
1.5%
sc/paris 7
 
1.3%
17608 5
 
0.9%
2 5
 
0.9%
a/5 5
 
0.9%
w./c 5
 
0.9%
f.c.c 4
 
0.7%
Other values (383) 445
82.0%
2024-03-21T09:54:14.080693image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3 364
12.7%
1 311
10.8%
2 268
9.3%
7 207
 
7.2%
6 206
 
7.2%
0 204
 
7.1%
5 195
 
6.8%
4 188
 
6.5%
8 144
 
5.0%
9 137
 
4.8%
Other values (22) 650
22.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2224
77.4%
Uppercase Letter 349
 
12.1%
Other Punctuation 172
 
6.0%
Space Separator 125
 
4.3%
Lowercase Letter 4
 
0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
C 92
26.4%
P 52
14.9%
A 51
14.6%
O 44
12.6%
S 40
11.5%
T 14
 
4.0%
N 14
 
4.0%
Q 12
 
3.4%
R 7
 
2.0%
I 7
 
2.0%
Other values (5) 16
 
4.6%
Decimal Number
ValueCountFrequency (%)
3 364
16.4%
1 311
14.0%
2 268
12.1%
7 207
9.3%
6 206
9.3%
0 204
9.2%
5 195
8.8%
4 188
8.5%
8 144
 
6.5%
9 137
 
6.2%
Lowercase Letter
ValueCountFrequency (%)
a 1
25.0%
r 1
25.0%
i 1
25.0%
s 1
25.0%
Other Punctuation
ValueCountFrequency (%)
. 126
73.3%
/ 46
 
26.7%
Space Separator
ValueCountFrequency (%)
125
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2521
87.7%
Latin 353
 
12.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
C 92
26.1%
P 52
14.7%
A 51
14.4%
O 44
12.5%
S 40
11.3%
T 14
 
4.0%
N 14
 
4.0%
Q 12
 
3.4%
R 7
 
2.0%
I 7
 
2.0%
Other values (9) 20
 
5.7%
Common
ValueCountFrequency (%)
3 364
14.4%
1 311
12.3%
2 268
10.6%
7 207
8.2%
6 206
8.2%
0 204
8.1%
5 195
7.7%
4 188
7.5%
8 144
 
5.7%
9 137
 
5.4%
Other values (3) 297
11.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2874
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 364
12.7%
1 311
10.8%
2 268
9.3%
7 207
 
7.2%
6 206
 
7.2%
0 204
 
7.1%
5 195
 
6.8%
4 188
 
6.5%
8 144
 
5.0%
9 137
 
4.8%
Other values (22) 650
22.6%

Fare
Real number (ℝ)

Distinct168
Distinct (%)40.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.854895
Minimum3.1708
Maximum512.3292
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.4 KiB
2024-03-21T09:54:14.450703image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum3.1708
5-th percentile7.2292
Q17.8958
median14.45625
Q331.5
95-th percentile151.55
Maximum512.3292
Range509.1584
Interquartile range (IQR)23.6042

Descriptive statistics

Standard deviation55.830334
Coefficient of variation (CV)1.5571189
Kurtosis17.925359
Mean35.854895
Median Absolute Deviation (MAD)6.81665
Skewness3.6827573
Sum14987.346
Variance3117.0262
MonotonicityNot monotonic
2024-03-21T09:54:14.711005image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7.75 21
 
5.0%
26 19
 
4.5%
8.05 18
 
4.3%
13 17
 
4.1%
10.5 11
 
2.6%
7.8958 11
 
2.6%
7.775 10
 
2.4%
7.2292 9
 
2.2%
7.225 9
 
2.2%
7.8542 8
 
1.9%
Other values (158) 285
68.2%
ValueCountFrequency (%)
3.1708 1
 
0.2%
6.4375 2
 
0.5%
6.4958 1
 
0.2%
6.95 1
 
0.2%
7 2
 
0.5%
7.05 2
 
0.5%
7.225 9
2.2%
7.2292 9
2.2%
7.25 5
1.2%
7.2833 1
 
0.2%
ValueCountFrequency (%)
512.3292 1
 
0.2%
263 2
 
0.5%
262.375 5
1.2%
247.5208 1
 
0.2%
227.525 1
 
0.2%
221.7792 3
0.7%
211.5 4
1.0%
211.3375 1
 
0.2%
164.8667 2
 
0.5%
151.55 2
 
0.5%

Cabin
Text

MISSING 

Distinct76
Distinct (%)83.5%
Missing327
Missing (%)78.2%
Memory size15.8 KiB
2024-03-21T09:54:14.969315image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length15
Median length3
Mean length4.0769231
Min length1

Characters and Unicode

Total characters371
Distinct characters18
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique62 ?
Unique (%)68.1%

Sample

1st rowB45
2nd rowE31
3rd rowB57 B59 B63 B66
4th rowB36
5th rowA21
ValueCountFrequency (%)
f 4
 
3.4%
b57 3
 
2.5%
b63 3
 
2.5%
b66 3
 
2.5%
b59 3
 
2.5%
c27 2
 
1.7%
e46 2
 
1.7%
c6 2
 
1.7%
c78 2
 
1.7%
b45 2
 
1.7%
Other values (80) 92
78.0%
2024-03-21T09:54:15.406183image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
C 43
11.6%
5 34
9.2%
1 33
 
8.9%
B 32
 
8.6%
6 30
 
8.1%
3 28
 
7.5%
27
 
7.3%
2 25
 
6.7%
4 21
 
5.7%
7 15
 
4.0%
Other values (8) 83
22.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 226
60.9%
Uppercase Letter 118
31.8%
Space Separator 27
 
7.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 34
15.0%
1 33
14.6%
6 30
13.3%
3 28
12.4%
2 25
11.1%
4 21
9.3%
7 15
6.6%
8 14
6.2%
0 14
6.2%
9 12
 
5.3%
Uppercase Letter
ValueCountFrequency (%)
C 43
36.4%
B 32
27.1%
D 14
 
11.9%
E 12
 
10.2%
F 8
 
6.8%
A 7
 
5.9%
G 2
 
1.7%
Space Separator
ValueCountFrequency (%)
27
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 253
68.2%
Latin 118
31.8%

Most frequent character per script

Common
ValueCountFrequency (%)
5 34
13.4%
1 33
13.0%
6 30
11.9%
3 28
11.1%
27
10.7%
2 25
9.9%
4 21
8.3%
7 15
5.9%
8 14
5.5%
0 14
5.5%
Latin
ValueCountFrequency (%)
C 43
36.4%
B 32
27.1%
D 14
 
11.9%
E 12
 
10.2%
F 8
 
6.8%
A 7
 
5.9%
G 2
 
1.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 371
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
C 43
11.6%
5 34
9.2%
1 33
 
8.9%
B 32
 
8.6%
6 30
 
8.1%
3 28
 
7.5%
27
 
7.3%
2 25
 
6.7%
4 21
 
5.7%
7 15
 
4.0%
Other values (8) 83
22.4%

Embarked
Categorical

Distinct3
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size23.8 KiB
S
270 
C
102 
Q
46 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters418
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowQ
2nd rowS
3rd rowQ
4th rowS
5th rowS

Common Values

ValueCountFrequency (%)
S 270
64.6%
C 102
 
24.4%
Q 46
 
11.0%

Length

2024-03-21T09:54:15.590656image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-21T09:54:15.729285image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
s 270
64.6%
c 102
 
24.4%
q 46
 
11.0%

Most occurring characters

ValueCountFrequency (%)
S 270
64.6%
C 102
 
24.4%
Q 46
 
11.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 418
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
S 270
64.6%
C 102
 
24.4%
Q 46
 
11.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 418
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
S 270
64.6%
C 102
 
24.4%
Q 46
 
11.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 418
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
S 270
64.6%
C 102
 
24.4%
Q 46
 
11.0%

DatasetName
Categorical

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size25.0 KiB
test
418 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters1672
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowtest
2nd rowtest
3rd rowtest
4th rowtest
5th rowtest

Common Values

ValueCountFrequency (%)
test 418
100.0%

Length

2024-03-21T09:54:15.881907image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-21T09:54:16.008535image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
test 418
100.0%

Most occurring characters

ValueCountFrequency (%)
t 836
50.0%
e 418
25.0%
s 418
25.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1672
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 836
50.0%
e 418
25.0%
s 418
25.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1672
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
t 836
50.0%
e 418
25.0%
s 418
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1672
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t 836
50.0%
e 418
25.0%
s 418
25.0%

Title
Categorical

Distinct5
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size24.5 KiB
Mr
240 
Miss
80 
Mrs
72 
Master
 
21
Other
 
5

Length

Max length6
Median length2
Mean length2.791866
Min length2

Characters and Unicode

Total characters1167
Distinct characters9
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMr
2nd rowMrs
3rd rowMr
4th rowMr
5th rowMrs

Common Values

ValueCountFrequency (%)
Mr 240
57.4%
Miss 80
 
19.1%
Mrs 72
 
17.2%
Master 21
 
5.0%
Other 5
 
1.2%

Length

2024-03-21T09:54:16.169109image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-21T09:54:16.378546image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
mr 240
57.4%
miss 80
 
19.1%
mrs 72
 
17.2%
master 21
 
5.0%
other 5
 
1.2%

Most occurring characters

ValueCountFrequency (%)
M 413
35.4%
r 338
29.0%
s 253
21.7%
i 80
 
6.9%
t 26
 
2.2%
e 26
 
2.2%
a 21
 
1.8%
O 5
 
0.4%
h 5
 
0.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 749
64.2%
Uppercase Letter 418
35.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r 338
45.1%
s 253
33.8%
i 80
 
10.7%
t 26
 
3.5%
e 26
 
3.5%
a 21
 
2.8%
h 5
 
0.7%
Uppercase Letter
ValueCountFrequency (%)
M 413
98.8%
O 5
 
1.2%

Most occurring scripts

ValueCountFrequency (%)
Latin 1167
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
M 413
35.4%
r 338
29.0%
s 253
21.7%
i 80
 
6.9%
t 26
 
2.2%
e 26
 
2.2%
a 21
 
1.8%
O 5
 
0.4%
h 5
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1167
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
M 413
35.4%
r 338
29.0%
s 253
21.7%
i 80
 
6.9%
t 26
 
2.2%
e 26
 
2.2%
a 21
 
1.8%
O 5
 
0.4%
h 5
 
0.4%

FamilySize
Real number (ℝ)

Distinct9
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.8397129
Minimum1
Maximum11
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.4 KiB
2024-03-21T09:54:16.587025image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q32
95-th percentile4
Maximum11
Range10
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.519072
Coefficient of variation (CV)0.82571144
Kurtosis13.431226
Mean1.8397129
Median Absolute Deviation (MAD)0
Skewness3.1685425
Sum769
Variance2.3075798
MonotonicityNot monotonic
2024-03-21T09:54:16.756536image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
1 253
60.5%
2 74
 
17.7%
3 57
 
13.6%
4 14
 
3.3%
5 7
 
1.7%
7 4
 
1.0%
11 4
 
1.0%
6 3
 
0.7%
8 2
 
0.5%
ValueCountFrequency (%)
1 253
60.5%
2 74
 
17.7%
3 57
 
13.6%
4 14
 
3.3%
5 7
 
1.7%
6 3
 
0.7%
7 4
 
1.0%
8 2
 
0.5%
11 4
 
1.0%
ValueCountFrequency (%)
11 4
 
1.0%
8 2
 
0.5%
7 4
 
1.0%
6 3
 
0.7%
5 7
 
1.7%
4 14
 
3.3%
3 57
 
13.6%
2 74
 
17.7%
1 253
60.5%

IsAlone
Categorical

Distinct2
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size23.8 KiB
1
253 
0
165 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters418
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row1
4th row1
5th row0

Common Values

ValueCountFrequency (%)
1 253
60.5%
0 165
39.5%

Length

2024-03-21T09:54:16.932065image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-21T09:54:17.093667image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
1 253
60.5%
0 165
39.5%

Most occurring characters

ValueCountFrequency (%)
1 253
60.5%
0 165
39.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 418
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 253
60.5%
0 165
39.5%

Most occurring scripts

ValueCountFrequency (%)
Common 418
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 253
60.5%
0 165
39.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 418
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 253
60.5%
0 165
39.5%

AgeGroup
Categorical

Distinct5
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size25.9 KiB
Adult
251 
MiddleAge
77 
Teenager
49 
Child
27 
Senior
 
14

Length

Max length9
Median length5
Mean length6.1220096
Min length5

Characters and Unicode

Total characters2559
Distinct characters17
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAdult
2nd rowMiddleAge
3rd rowSenior
4th rowAdult
5th rowAdult

Common Values

ValueCountFrequency (%)
Adult 251
60.0%
MiddleAge 77
 
18.4%
Teenager 49
 
11.7%
Child 27
 
6.5%
Senior 14
 
3.3%

Length

2024-03-21T09:54:17.297090image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-21T09:54:17.529470image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
adult 251
60.0%
middleage 77
 
18.4%
teenager 49
 
11.7%
child 27
 
6.5%
senior 14
 
3.3%

Most occurring characters

ValueCountFrequency (%)
d 432
16.9%
l 355
13.9%
A 328
12.8%
e 315
12.3%
u 251
9.8%
t 251
9.8%
g 126
 
4.9%
i 118
 
4.6%
M 77
 
3.0%
n 63
 
2.5%
Other values (7) 243
9.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2064
80.7%
Uppercase Letter 495
 
19.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
d 432
20.9%
l 355
17.2%
e 315
15.3%
u 251
12.2%
t 251
12.2%
g 126
 
6.1%
i 118
 
5.7%
n 63
 
3.1%
r 63
 
3.1%
a 49
 
2.4%
Other values (2) 41
 
2.0%
Uppercase Letter
ValueCountFrequency (%)
A 328
66.3%
M 77
 
15.6%
T 49
 
9.9%
C 27
 
5.5%
S 14
 
2.8%

Most occurring scripts

ValueCountFrequency (%)
Latin 2559
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
d 432
16.9%
l 355
13.9%
A 328
12.8%
e 315
12.3%
u 251
9.8%
t 251
9.8%
g 126
 
4.9%
i 118
 
4.6%
M 77
 
3.0%
n 63
 
2.5%
Other values (7) 243
9.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2559
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
d 432
16.9%
l 355
13.9%
A 328
12.8%
e 315
12.3%
u 251
9.8%
t 251
9.8%
g 126
 
4.9%
i 118
 
4.6%
M 77
 
3.0%
n 63
 
2.5%
Other values (7) 243
9.5%

FarePerPerson
Real number (ℝ)

Distinct190
Distinct (%)45.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.064937
Minimum1.1107143
Maximum262.375
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.4 KiB
2024-03-21T09:54:17.741904image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum1.1107143
5-th percentile4.5779167
Q17.6541667
median8.6625
Q326
95-th percentile73.761255
Maximum262.375
Range261.26429
Interquartile range (IQR)18.345833

Descriptive statistics

Standard deviation35.675816
Coefficient of variation (CV)1.6168556
Kurtosis21.828014
Mean22.064937
Median Absolute Deviation (MAD)3.0041667
Skewness4.3284248
Sum9223.1436
Variance1272.7639
MonotonicityNot monotonic
2024-03-21T09:54:17.966330image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13 28
 
6.7%
7.75 22
 
5.3%
8.05 20
 
4.8%
10.5 16
 
3.8%
7.8958 11
 
2.6%
7.775 10
 
2.4%
7.225 9
 
2.2%
7.8542 8
 
1.9%
7.2292 8
 
1.9%
26 7
 
1.7%
Other values (180) 279
66.7%
ValueCountFrequency (%)
1.110714286 1
0.2%
1.5854 1
0.2%
2.409733333 1
0.2%
2.683333333 1
0.2%
2.8389 1
0.2%
2.8875 1
0.2%
3.21875 1
0.2%
3.2479 1
0.2%
3.5 1
0.2%
3.875 1
0.2%
ValueCountFrequency (%)
262.375 2
0.5%
256.1646 1
0.2%
221.7792 1
0.2%
211.5 2
0.5%
211.3375 1
0.2%
164.8667 1
0.2%
151.55 1
0.2%
135.6333 1
0.2%
134.5 1
0.2%
113.7625 1
0.2%

FareQuartile
Categorical

Distinct4
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size23.8 KiB
0
117 
3
107 
1
97 
2
97 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters418
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0 117
28.0%
3 107
25.6%
1 97
23.2%
2 97
23.2%

Length

2024-03-21T09:54:18.188707image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-21T09:54:18.381194image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
0 117
28.0%
3 107
25.6%
1 97
23.2%
2 97
23.2%

Most occurring characters

ValueCountFrequency (%)
0 117
28.0%
3 107
25.6%
1 97
23.2%
2 97
23.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 418
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 117
28.0%
3 107
25.6%
1 97
23.2%
2 97
23.2%

Most occurring scripts

ValueCountFrequency (%)
Common 418
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 117
28.0%
3 107
25.6%
1 97
23.2%
2 97
23.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 418
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 117
28.0%
3 107
25.6%
1 97
23.2%
2 97
23.2%
Distinct4
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size23.8 KiB
3
108 
1
107 
0
105 
2
98 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters418
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row2
4th row1
5th row0

Common Values

ValueCountFrequency (%)
3 108
25.8%
1 107
25.6%
0 105
25.1%
2 98
23.4%

Length

2024-03-21T09:54:18.561710image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-21T09:54:18.732252image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
3 108
25.8%
1 107
25.6%
0 105
25.1%
2 98
23.4%

Most occurring characters

ValueCountFrequency (%)
3 108
25.8%
1 107
25.6%
0 105
25.1%
2 98
23.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 418
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 108
25.8%
1 107
25.6%
0 105
25.1%
2 98
23.4%

Most occurring scripts

ValueCountFrequency (%)
Common 418
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3 108
25.8%
1 107
25.6%
0 105
25.1%
2 98
23.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 418
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 108
25.8%
1 107
25.6%
0 105
25.1%
2 98
23.4%

CabinPrefix
Categorical

Distinct7
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Memory size23.8 KiB
F
217 
E
64 
C
62 
G
37 
B
 
18
Other values (2)
 
20

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters418
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowF
2nd rowF
3rd rowF
4th rowE
5th rowE

Common Values

ValueCountFrequency (%)
F 217
51.9%
E 64
 
15.3%
C 62
 
14.8%
G 37
 
8.9%
B 18
 
4.3%
D 13
 
3.1%
A 7
 
1.7%

Length

2024-03-21T09:54:18.937733image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-21T09:54:19.157115image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
f 217
51.9%
e 64
 
15.3%
c 62
 
14.8%
g 37
 
8.9%
b 18
 
4.3%
d 13
 
3.1%
a 7
 
1.7%

Most occurring characters

ValueCountFrequency (%)
F 217
51.9%
E 64
 
15.3%
C 62
 
14.8%
G 37
 
8.9%
B 18
 
4.3%
D 13
 
3.1%
A 7
 
1.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 418
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
F 217
51.9%
E 64
 
15.3%
C 62
 
14.8%
G 37
 
8.9%
B 18
 
4.3%
D 13
 
3.1%
A 7
 
1.7%

Most occurring scripts

ValueCountFrequency (%)
Latin 418
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
F 217
51.9%
E 64
 
15.3%
C 62
 
14.8%
G 37
 
8.9%
B 18
 
4.3%
D 13
 
3.1%
A 7
 
1.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 418
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
F 217
51.9%
E 64
 
15.3%
C 62
 
14.8%
G 37
 
8.9%
B 18
 
4.3%
D 13
 
3.1%
A 7
 
1.7%

Interactions

2024-03-21T09:54:09.185781image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-21T09:54:03.780237image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-21T09:54:04.792531image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-21T09:54:05.680158image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-21T09:54:06.578788image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-21T09:54:07.439452image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-21T09:54:08.278237image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-21T09:54:09.309484image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-21T09:54:03.900914image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-21T09:54:04.926172image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-21T09:54:05.803826image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-21T09:54:06.687496image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-21T09:54:07.555177image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-21T09:54:08.387945image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-21T09:54:09.432155image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-21T09:54:04.057531image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-21T09:54:05.053864image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-21T09:54:05.937502image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-21T09:54:06.818147image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-21T09:54:07.682803image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-21T09:54:08.512617image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-21T09:54:09.556822image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-21T09:54:04.249982image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-21T09:54:05.191465image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-21T09:54:06.073108image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-21T09:54:06.945812image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-21T09:54:07.819436image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-21T09:54:08.637251image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-21T09:54:09.669520image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-21T09:54:04.384620image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-21T09:54:05.310146image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-21T09:54:06.200804image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-21T09:54:07.055480image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-21T09:54:07.927181image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-21T09:54:08.848683image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-21T09:54:09.792163image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-21T09:54:04.521256image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-21T09:54:05.434814image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-21T09:54:06.334442image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-21T09:54:07.191116image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-21T09:54:08.041840image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-21T09:54:08.963377image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-21T09:54:09.902897image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-21T09:54:04.653902image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-21T09:54:05.559512image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-21T09:54:06.453090image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-21T09:54:07.314787image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-21T09:54:08.156533image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-03-21T09:54:09.070090image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Missing values

2024-03-21T09:54:10.111337image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-21T09:54:10.478326image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

PassengerIdSurvivedPclassNameSexAgeSibSpParchTicketFareCabinEmbarkedDatasetNameTitleFamilySizeIsAloneAgeGroupFarePerPersonFareQuartileFarePerPersonQuartileCabinPrefix
0892NaN3Kelly, Mr. Jamesmale34.5003309117.8292NaNQtestMr11Adult7.82920001F
1893NaN3Wilkes, Mrs. James (Ellen Needs)female47.0103632727.0000NaNStestMrs20MiddleAge3.50000000F
2894NaN2Myles, Mr. Thomas Francismale62.0002402769.6875NaNQtestMr11Senior9.68750012F
3895NaN3Wirz, Mr. Albertmale27.0003151548.6625NaNStestMr11Adult8.66250011E
4896NaN3Hirvonen, Mrs. Alexander (Helga E Lindqvist)female22.011310129812.2875NaNStestMrs30Adult4.09583310E
5897NaN3Svensson, Mr. Johan Cervinmale14.00075389.2250NaNStestMr11Teenager9.22500012E
6898NaN3Connolly, Miss. Katefemale30.0003309727.6292NaNQtestMiss11Adult7.62920000F
7899NaN2Caldwell, Mr. Albert Francismale26.01124873829.0000NaNStestMr30Adult9.66666722F
8900NaN3Abrahim, Mrs. Joseph (Sophie Halaut Easu)female18.00026577.2292NaNCtestMrs11Teenager7.22920000F
9901NaN3Davies, Mr. John Samuelmale21.020A/4 4887124.1500NaNStestMr30Adult8.05000021G
PassengerIdSurvivedPclassNameSexAgeSibSpParchTicketFareCabinEmbarkedDatasetNameTitleFamilySizeIsAloneAgeGroupFarePerPersonFareQuartileFarePerPersonQuartileCabinPrefix
4081300NaN3Riordan, Miss. Johanna Hannah""female18.0003349157.7208NaNQtestMiss11Teenager7.72080001F
4091301NaN3Peacock, Miss. Treasteallfemale3.011SOTON/O.Q. 310131513.7750NaNStestMiss30Child4.59166710E
4101302NaN3Naughton, Miss. Hannahfemale18.0003652377.7500NaNQtestMiss11Teenager7.75000001F
4111303NaN1Minahan, Mrs. William Edward (Lillian E Thorpe)female37.0101992890.0000C78QtestMrs20Adult45.00000033C
4121304NaN3Henriksson, Miss. Jenny Lovisafemale28.0003470867.7750NaNStestMiss11Adult7.77500001F
4131305NaN3Spector, Mr. Woolfmale26.000A.5. 32368.0500NaNStestMr11Adult8.05000011E
4141306NaN1Oliva y Ocana, Dona. Ferminafemale39.000PC 17758108.9000C105CtestMiss11Adult108.90000033C
4151307NaN3Saether, Mr. Simon Sivertsenmale38.500SOTON/O.Q. 31012627.2500NaNStestMr11Adult7.25000000F
4161308NaN3Ware, Mr. Frederickmale26.0003593098.0500NaNStestMr11Adult8.05000011E
4171309NaN3Peter, Master. Michael Jmale6.011266822.3583NaNCtestMaster30Child7.45276720G